Overview

Dataset statistics

Number of variables33
Number of observations736
Missing cells129
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory189.9 KiB
Average record size in memory264.2 B

Variable types

Categorical21
Numeric7
Boolean5

Alerts

Permissions has constant value "I understand."Constant
Timestamp has a high cardinality: 735 distinct valuesHigh cardinality
Anxiety is highly overall correlated with DepressionHigh correlation
Depression is highly overall correlated with AnxietyHigh correlation
Frequency [Hip hop] is highly overall correlated with Frequency [Rap]High correlation
Frequency [Rap] is highly overall correlated with Frequency [Hip hop]High correlation
BPM has 107 (14.5%) missing valuesMissing
Music effects has 8 (1.1%) missing valuesMissing
BPM is highly skewed (γ1 = 25.07987241)Skewed
Timestamp is uniformly distributedUniform
Anxiety has 35 (4.8%) zerosZeros
Depression has 84 (11.4%) zerosZeros
Insomnia has 149 (20.2%) zerosZeros
OCD has 248 (33.7%) zerosZeros

Reproduction

Analysis started2023-07-14 06:23:02.715543
Analysis finished2023-07-14 06:30:30.612056
Duration7 minutes and 27.9 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct735
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
8/28/2022 16:15:08
 
2
8/27/2022 19:29:02
 
1
9/1/2022 21:07:33
 
1
9/1/2022 19:09:32
 
1
9/1/2022 19:36:54
 
1
Other values (730)
730 

Length

Max length19
Median length18
Mean length17.514946
Min length16

Characters and Unicode

Total characters12891
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique734 ?
Unique (%)99.7%

Sample

1st row8/27/2022 19:29:02
2nd row8/27/2022 19:57:31
3rd row8/27/2022 21:28:18
4th row8/27/2022 21:40:40
5th row8/27/2022 21:54:47

Common Values

ValueCountFrequency (%)
8/28/2022 16:15:08 2
 
0.3%
8/27/2022 19:29:02 1
 
0.1%
9/1/2022 21:07:33 1
 
0.1%
9/1/2022 19:09:32 1
 
0.1%
9/1/2022 19:36:54 1
 
0.1%
9/1/2022 19:39:07 1
 
0.1%
9/1/2022 19:39:49 1
 
0.1%
9/1/2022 19:44:33 1
 
0.1%
9/1/2022 20:02:19 1
 
0.1%
9/1/2022 20:36:10 1
 
0.1%
Other values (725) 725
98.5%

Length

2023-07-14T12:30:33.258883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8/28/2022 235
 
16.0%
8/29/2022 153
 
10.4%
9/1/2022 54
 
3.7%
9/2/2022 37
 
2.5%
9/12/2022 31
 
2.1%
9/3/2022 22
 
1.5%
8/27/2022 21
 
1.4%
8/30/2022 21
 
1.4%
9/13/2022 18
 
1.2%
8/31/2022 16
 
1.1%
Other values (767) 864
58.7%

Most occurring characters

ValueCountFrequency (%)
2 3331
25.8%
/ 1472
11.4%
: 1472
11.4%
0 1316
 
10.2%
1 1031
 
8.0%
8 917
 
7.1%
736
 
5.7%
9 645
 
5.0%
3 588
 
4.6%
4 489
 
3.8%
Other values (3) 894
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9211
71.5%
Other Punctuation 2944
 
22.8%
Space Separator 736
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3331
36.2%
0 1316
 
14.3%
1 1031
 
11.2%
8 917
 
10.0%
9 645
 
7.0%
3 588
 
6.4%
4 489
 
5.3%
5 424
 
4.6%
7 236
 
2.6%
6 234
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 1472
50.0%
: 1472
50.0%
Space Separator
ValueCountFrequency (%)
736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3331
25.8%
/ 1472
11.4%
: 1472
11.4%
0 1316
 
10.2%
1 1031
 
8.0%
8 917
 
7.1%
736
 
5.7%
9 645
 
5.0%
3 588
 
4.6%
4 489
 
3.8%
Other values (3) 894
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3331
25.8%
/ 1472
11.4%
: 1472
11.4%
0 1316
 
10.2%
1 1031
 
8.0%
8 917
 
7.1%
736
 
5.7%
9 645
 
5.0%
3 588
 
4.6%
4 489
 
3.8%
Other values (3) 894
 
6.9%

Age
Real number (ℝ)

Distinct61
Distinct (%)8.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean25.206803
Minimum10
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:33.463219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q118
median21
Q328
95-th percentile56
Maximum89
Range79
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.05497
Coefficient of variation (CV)0.47824272
Kurtosis4.6757794
Mean25.206803
Median Absolute Deviation (MAD)4
Skewness2.1249226
Sum18527
Variance145.3223
MonotonicityNot monotonic
2023-07-14T12:30:33.685485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 85
 
11.5%
19 61
 
8.3%
17 59
 
8.0%
21 52
 
7.1%
16 44
 
6.0%
20 40
 
5.4%
22 39
 
5.3%
23 37
 
5.0%
26 22
 
3.0%
25 22
 
3.0%
Other values (51) 274
37.2%
ValueCountFrequency (%)
10 1
 
0.1%
12 3
 
0.4%
13 8
 
1.1%
14 17
 
2.3%
15 21
 
2.9%
16 44
6.0%
17 59
8.0%
18 85
11.5%
19 61
8.3%
20 40
5.4%
ValueCountFrequency (%)
89 1
 
0.1%
80 1
 
0.1%
74 1
 
0.1%
73 1
 
0.1%
72 1
 
0.1%
71 1
 
0.1%
70 1
 
0.1%
69 1
 
0.1%
68 1
 
0.1%
67 3
0.4%

Primary streaming service
Categorical

Distinct6
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Memory size5.9 KiB
Spotify
458 
YouTube Music
94 
I do not use a streaming service.
71 
Apple Music
51 
Other streaming service
50 

Length

Max length33
Median length7
Mean length11.644898
Min length7

Characters and Unicode

Total characters8559
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpotify
2nd rowPandora
3rd rowSpotify
4th rowYouTube Music
5th rowSpotify

Common Values

ValueCountFrequency (%)
Spotify 458
62.2%
YouTube Music 94
 
12.8%
I do not use a streaming service. 71
 
9.6%
Apple Music 51
 
6.9%
Other streaming service 50
 
6.8%
Pandora 11
 
1.5%
(Missing) 1
 
0.1%

Length

2023-07-14T12:30:33.896158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:34.245907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
spotify 458
32.6%
music 145
 
10.3%
streaming 121
 
8.6%
service 121
 
8.6%
youtube 94
 
6.7%
i 71
 
5.0%
do 71
 
5.0%
not 71
 
5.0%
use 71
 
5.0%
a 71
 
5.0%
Other values (3) 112
 
8.0%

Most occurring characters

ValueCountFrequency (%)
i 845
 
9.9%
o 705
 
8.2%
t 700
 
8.2%
671
 
7.8%
e 629
 
7.3%
p 560
 
6.5%
S 458
 
5.4%
f 458
 
5.4%
y 458
 
5.4%
s 458
 
5.4%
Other values (20) 2617
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6843
80.0%
Uppercase Letter 974
 
11.4%
Space Separator 671
 
7.8%
Other Punctuation 71
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 845
12.3%
o 705
10.3%
t 700
10.2%
e 629
9.2%
p 560
8.2%
f 458
 
6.7%
y 458
 
6.7%
s 458
 
6.7%
u 404
 
5.9%
r 303
 
4.4%
Other values (10) 1323
19.3%
Uppercase Letter
ValueCountFrequency (%)
S 458
47.0%
M 145
 
14.9%
Y 94
 
9.7%
T 94
 
9.7%
I 71
 
7.3%
A 51
 
5.2%
O 50
 
5.1%
P 11
 
1.1%
Space Separator
ValueCountFrequency (%)
671
100.0%
Other Punctuation
ValueCountFrequency (%)
. 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7817
91.3%
Common 742
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 845
 
10.8%
o 705
 
9.0%
t 700
 
9.0%
e 629
 
8.0%
p 560
 
7.2%
S 458
 
5.9%
f 458
 
5.9%
y 458
 
5.9%
s 458
 
5.9%
u 404
 
5.2%
Other values (18) 2142
27.4%
Common
ValueCountFrequency (%)
671
90.4%
. 71
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 845
 
9.9%
o 705
 
8.2%
t 700
 
8.2%
671
 
7.8%
e 629
 
7.3%
p 560
 
6.5%
S 458
 
5.4%
f 458
 
5.4%
y 458
 
5.4%
s 458
 
5.4%
Other values (20) 2617
30.6%

Hours per day
Real number (ℝ)

Distinct27
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5727582
Minimum0
Maximum24
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:34.486040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0281988
Coefficient of variation (CV)0.84758013
Kurtosis10.466149
Mean3.5727582
Median Absolute Deviation (MAD)1
Skewness2.5325429
Sum2629.55
Variance9.1699882
MonotonicityNot monotonic
2023-07-14T12:30:34.675946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 173
23.5%
3 120
16.3%
1 117
15.9%
4 83
11.3%
5 54
 
7.3%
6 47
 
6.4%
8 29
 
3.9%
10 20
 
2.7%
0.5 20
 
2.7%
1.5 17
 
2.3%
Other values (17) 56
 
7.6%
ValueCountFrequency (%)
0 6
 
0.8%
0.1 1
 
0.1%
0.25 3
 
0.4%
0.5 20
 
2.7%
0.7 1
 
0.1%
1 117
15.9%
1.5 17
 
2.3%
2 173
23.5%
2.5 6
 
0.8%
3 120
16.3%
ValueCountFrequency (%)
24 3
 
0.4%
20 1
 
0.1%
18 1
 
0.1%
16 1
 
0.1%
15 2
 
0.3%
14 1
 
0.1%
13 1
 
0.1%
12 9
1.2%
11 1
 
0.1%
10 20
2.7%

While working
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.4%
Memory size1.6 KiB
True
579 
False
154 
(Missing)
 
3
ValueCountFrequency (%)
True 579
78.7%
False 154
 
20.9%
(Missing) 3
 
0.4%
2023-07-14T12:30:34.865931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Instrumentalist
Boolean

Distinct2
Distinct (%)0.3%
Missing4
Missing (%)0.5%
Memory size1.6 KiB
False
497 
True
235 
(Missing)
 
4
ValueCountFrequency (%)
False 497
67.5%
True 235
31.9%
(Missing) 4
 
0.5%
2023-07-14T12:30:35.024498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Composer
Boolean

Distinct2
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size1.6 KiB
False
609 
True
126 
(Missing)
 
1
ValueCountFrequency (%)
False 609
82.7%
True 126
 
17.1%
(Missing) 1
 
0.1%
2023-07-14T12:30:35.176758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Fav genre
Categorical

Distinct16
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Rock
188 
Pop
114 
Metal
88 
Classical
53 
Video game music
44 
Other values (11)
249 

Length

Max length16
Median length9
Mean length5.2146739
Min length3

Characters and Unicode

Total characters3838
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLatin
2nd rowRock
3rd rowVideo game music
4th rowJazz
5th rowR&B

Common Values

ValueCountFrequency (%)
Rock 188
25.5%
Pop 114
15.5%
Metal 88
12.0%
Classical 53
 
7.2%
Video game music 44
 
6.0%
EDM 37
 
5.0%
R&B 35
 
4.8%
Hip hop 35
 
4.8%
Folk 30
 
4.1%
K pop 26
 
3.5%
Other values (6) 86
11.7%

Length

2023-07-14T12:30:35.330109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rock 188
21.2%
pop 140
15.8%
metal 88
9.9%
classical 53
 
6.0%
video 44
 
5.0%
game 44
 
5.0%
music 44
 
5.0%
edm 37
 
4.2%
hop 35
 
4.0%
hip 35
 
4.0%
Other values (9) 177
20.0%

Most occurring characters

ValueCountFrequency (%)
o 478
 
12.5%
c 285
 
7.4%
a 283
 
7.4%
p 264
 
6.9%
R 245
 
6.4%
l 230
 
6.0%
k 218
 
5.7%
i 189
 
4.9%
e 182
 
4.7%
s 156
 
4.1%
Other values (26) 1308
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2809
73.2%
Uppercase Letter 845
 
22.0%
Space Separator 149
 
3.9%
Other Punctuation 35
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 478
17.0%
c 285
10.1%
a 283
10.1%
p 264
9.4%
l 230
8.2%
k 218
7.8%
i 189
 
6.7%
e 182
 
6.5%
s 156
 
5.6%
t 116
 
4.1%
Other values (10) 408
14.5%
Uppercase Letter
ValueCountFrequency (%)
R 245
29.0%
M 125
14.8%
P 114
13.5%
C 78
 
9.2%
V 44
 
5.2%
E 37
 
4.4%
D 37
 
4.4%
H 35
 
4.1%
B 35
 
4.1%
F 30
 
3.6%
Other values (4) 65
 
7.7%
Space Separator
ValueCountFrequency (%)
149
100.0%
Other Punctuation
ValueCountFrequency (%)
& 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3654
95.2%
Common 184
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 478
13.1%
c 285
 
7.8%
a 283
 
7.7%
p 264
 
7.2%
R 245
 
6.7%
l 230
 
6.3%
k 218
 
6.0%
i 189
 
5.2%
e 182
 
5.0%
s 156
 
4.3%
Other values (24) 1124
30.8%
Common
ValueCountFrequency (%)
149
81.0%
& 35
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 478
 
12.5%
c 285
 
7.4%
a 283
 
7.4%
p 264
 
6.9%
R 245
 
6.4%
l 230
 
6.0%
k 218
 
5.7%
i 189
 
4.9%
e 182
 
4.7%
s 156
 
4.1%
Other values (26) 1308
34.1%

Exploratory
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
True
525 
False
211 
ValueCountFrequency (%)
True 525
71.3%
False 211
28.7%
2023-07-14T12:30:35.681000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Foreign languages
Boolean

Distinct2
Distinct (%)0.3%
Missing4
Missing (%)0.5%
Memory size1.6 KiB
True
404 
False
328 
(Missing)
 
4
ValueCountFrequency (%)
True 404
54.9%
False 328
44.6%
(Missing) 4
 
0.5%
2023-07-14T12:30:35.845604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

BPM
Real number (ℝ)

MISSING  SKEWED 

Distinct135
Distinct (%)21.5%
Missing107
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean1589948.3
Minimum0
Maximum1 × 109
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:36.135350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1100
median120
Q3144
95-th percentile180
Maximum1 × 109
Range1 × 109
Interquartile range (IQR)44

Descriptive statistics

Standard deviation39872606
Coefficient of variation (CV)25.077926
Kurtosis629
Mean1589948.3
Median Absolute Deviation (MAD)21
Skewness25.079872
Sum1.0000775 × 109
Variance1.5898247 × 1015
MonotonicityNot monotonic
2023-07-14T12:30:36.387177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 45
 
6.1%
140 25
 
3.4%
150 18
 
2.4%
110 16
 
2.2%
105 15
 
2.0%
130 13
 
1.8%
100 11
 
1.5%
80 11
 
1.5%
136 11
 
1.5%
90 11
 
1.5%
Other values (125) 453
61.5%
(Missing) 107
 
14.5%
ValueCountFrequency (%)
0 3
0.4%
4 1
 
0.1%
8 1
 
0.1%
20 1
 
0.1%
40 1
 
0.1%
52 1
 
0.1%
55 2
0.3%
56 1
 
0.1%
60 2
0.3%
61 1
 
0.1%
ValueCountFrequency (%)
999999999 1
 
0.1%
624 1
 
0.1%
220 2
 
0.3%
218 1
 
0.1%
210 2
 
0.3%
208 1
 
0.1%
204 1
 
0.1%
200 7
1.0%
194 2
 
0.3%
193 1
 
0.1%

Frequency [Classical]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Rarely
259 
Sometimes
200 
Never
169 
Very frequently
108 

Length

Max length15
Median length9
Mean length7.90625
Min length5

Characters and Unicode

Total characters5819
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowNever

Common Values

ValueCountFrequency (%)
Rarely 259
35.2%
Sometimes 200
27.2%
Never 169
23.0%
Very frequently 108
14.7%

Length

2023-07-14T12:30:36.596171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:36.762309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rarely 259
30.7%
sometimes 200
23.7%
never 169
20.0%
very 108
12.8%
frequently 108
12.8%

Most occurring characters

ValueCountFrequency (%)
e 1321
22.7%
r 644
11.1%
y 475
 
8.2%
m 400
 
6.9%
l 367
 
6.3%
t 308
 
5.3%
R 259
 
4.5%
a 259
 
4.5%
s 200
 
3.4%
i 200
 
3.4%
Other values (10) 1386
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4975
85.5%
Uppercase Letter 736
 
12.6%
Space Separator 108
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1321
26.6%
r 644
12.9%
y 475
 
9.5%
m 400
 
8.0%
l 367
 
7.4%
t 308
 
6.2%
a 259
 
5.2%
s 200
 
4.0%
i 200
 
4.0%
o 200
 
4.0%
Other values (5) 601
12.1%
Uppercase Letter
ValueCountFrequency (%)
R 259
35.2%
S 200
27.2%
N 169
23.0%
V 108
14.7%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5711
98.1%
Common 108
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1321
23.1%
r 644
11.3%
y 475
 
8.3%
m 400
 
7.0%
l 367
 
6.4%
t 308
 
5.4%
R 259
 
4.5%
a 259
 
4.5%
s 200
 
3.5%
i 200
 
3.5%
Other values (9) 1278
22.4%
Common
ValueCountFrequency (%)
108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1321
22.7%
r 644
11.1%
y 475
 
8.2%
m 400
 
6.9%
l 367
 
6.3%
t 308
 
5.3%
R 259
 
4.5%
a 259
 
4.5%
s 200
 
3.4%
i 200
 
3.4%
Other values (10) 1386
23.8%

Frequency [Country]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
343 
Rarely
233 
Sometimes
111 
Very frequently
49 

Length

Max length15
Median length9
Mean length6.5855978
Min length5

Characters and Unicode

Total characters4847
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowNever
3rd rowNever
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Never 343
46.6%
Rarely 233
31.7%
Sometimes 111
 
15.1%
Very frequently 49
 
6.7%

Length

2023-07-14T12:30:36.936992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:37.128598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 343
43.7%
rarely 233
29.7%
sometimes 111
 
14.1%
very 49
 
6.2%
frequently 49
 
6.2%

Most occurring characters

ValueCountFrequency (%)
e 1288
26.6%
r 674
13.9%
N 343
 
7.1%
v 343
 
7.1%
y 331
 
6.8%
l 282
 
5.8%
R 233
 
4.8%
a 233
 
4.8%
m 222
 
4.6%
t 160
 
3.3%
Other values (10) 738
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4062
83.8%
Uppercase Letter 736
 
15.2%
Space Separator 49
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1288
31.7%
r 674
16.6%
v 343
 
8.4%
y 331
 
8.1%
l 282
 
6.9%
a 233
 
5.7%
m 222
 
5.5%
t 160
 
3.9%
o 111
 
2.7%
i 111
 
2.7%
Other values (5) 307
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
N 343
46.6%
R 233
31.7%
S 111
 
15.1%
V 49
 
6.7%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4798
99.0%
Common 49
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1288
26.8%
r 674
14.0%
N 343
 
7.1%
v 343
 
7.1%
y 331
 
6.9%
l 282
 
5.9%
R 233
 
4.9%
a 233
 
4.9%
m 222
 
4.6%
t 160
 
3.3%
Other values (9) 689
14.4%
Common
ValueCountFrequency (%)
49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1288
26.6%
r 674
13.9%
N 343
 
7.1%
v 343
 
7.1%
y 331
 
6.8%
l 282
 
5.8%
R 233
 
4.8%
a 233
 
4.8%
m 222
 
4.6%
t 160
 
3.3%
Other values (10) 738
15.2%

Frequency [EDM]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
307 
Rarely
194 
Sometimes
146 
Very frequently
89 

Length

Max length15
Median length9
Mean length7.2663043
Min length5

Characters and Unicode

Total characters5348
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowNever
3rd rowVery frequently
4th rowNever
5th rowRarely

Common Values

ValueCountFrequency (%)
Never 307
41.7%
Rarely 194
26.4%
Sometimes 146
19.8%
Very frequently 89
 
12.1%

Length

2023-07-14T12:30:37.322943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:37.509793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 307
37.2%
rarely 194
23.5%
sometimes 146
17.7%
very 89
 
10.8%
frequently 89
 
10.8%

Most occurring characters

ValueCountFrequency (%)
e 1367
25.6%
r 679
12.7%
y 372
 
7.0%
N 307
 
5.7%
v 307
 
5.7%
m 292
 
5.5%
l 283
 
5.3%
t 235
 
4.4%
a 194
 
3.6%
R 194
 
3.6%
Other values (10) 1118
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4523
84.6%
Uppercase Letter 736
 
13.8%
Space Separator 89
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1367
30.2%
r 679
15.0%
y 372
 
8.2%
v 307
 
6.8%
m 292
 
6.5%
l 283
 
6.3%
t 235
 
5.2%
a 194
 
4.3%
o 146
 
3.2%
i 146
 
3.2%
Other values (5) 502
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
N 307
41.7%
R 194
26.4%
S 146
19.8%
V 89
 
12.1%
Space Separator
ValueCountFrequency (%)
89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5259
98.3%
Common 89
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1367
26.0%
r 679
12.9%
y 372
 
7.1%
N 307
 
5.8%
v 307
 
5.8%
m 292
 
5.6%
l 283
 
5.4%
t 235
 
4.5%
a 194
 
3.7%
R 194
 
3.7%
Other values (9) 1029
19.6%
Common
ValueCountFrequency (%)
89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1367
25.6%
r 679
12.7%
y 372
 
7.0%
N 307
 
5.7%
v 307
 
5.7%
m 292
 
5.5%
l 283
 
5.3%
t 235
 
4.4%
a 194
 
3.6%
R 194
 
3.6%
Other values (10) 1118
20.9%

Frequency [Folk]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
292 
Rarely
221 
Sometimes
145 
Very frequently
78 

Length

Max length15
Median length9
Mean length7.1480978
Min length5

Characters and Unicode

Total characters5261
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowRarely
3rd rowNever
4th rowRarely
5th rowNever

Common Values

ValueCountFrequency (%)
Never 292
39.7%
Rarely 221
30.0%
Sometimes 145
19.7%
Very frequently 78
 
10.6%

Length

2023-07-14T12:30:37.694983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:37.861984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 292
35.9%
rarely 221
27.1%
sometimes 145
17.8%
very 78
 
9.6%
frequently 78
 
9.6%

Most occurring characters

ValueCountFrequency (%)
e 1329
25.3%
r 669
12.7%
y 377
 
7.2%
l 299
 
5.7%
N 292
 
5.6%
v 292
 
5.6%
m 290
 
5.5%
t 223
 
4.2%
a 221
 
4.2%
R 221
 
4.2%
Other values (10) 1048
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4447
84.5%
Uppercase Letter 736
 
14.0%
Space Separator 78
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1329
29.9%
r 669
15.0%
y 377
 
8.5%
l 299
 
6.7%
v 292
 
6.6%
m 290
 
6.5%
t 223
 
5.0%
a 221
 
5.0%
o 145
 
3.3%
i 145
 
3.3%
Other values (5) 457
 
10.3%
Uppercase Letter
ValueCountFrequency (%)
N 292
39.7%
R 221
30.0%
S 145
19.7%
V 78
 
10.6%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5183
98.5%
Common 78
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1329
25.6%
r 669
12.9%
y 377
 
7.3%
l 299
 
5.8%
N 292
 
5.6%
v 292
 
5.6%
m 290
 
5.6%
t 223
 
4.3%
a 221
 
4.3%
R 221
 
4.3%
Other values (9) 970
18.7%
Common
ValueCountFrequency (%)
78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1329
25.3%
r 669
12.7%
y 377
 
7.2%
l 299
 
5.7%
N 292
 
5.6%
v 292
 
5.6%
m 290
 
5.5%
t 223
 
4.2%
a 221
 
4.2%
R 221
 
4.2%
Other values (10) 1048
19.9%

Frequency [Gospel]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
535 
Rarely
135 
Sometimes
 
52
Very frequently
 
14

Length

Max length15
Median length5
Mean length5.65625
Min length5

Characters and Unicode

Total characters4163
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowRarely

Common Values

ValueCountFrequency (%)
Never 535
72.7%
Rarely 135
 
18.3%
Sometimes 52
 
7.1%
Very frequently 14
 
1.9%

Length

2023-07-14T12:30:38.034978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:38.220412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 535
71.3%
rarely 135
 
18.0%
sometimes 52
 
6.9%
very 14
 
1.9%
frequently 14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 1351
32.5%
r 698
16.8%
N 535
 
12.9%
v 535
 
12.9%
y 163
 
3.9%
l 149
 
3.6%
R 135
 
3.2%
a 135
 
3.2%
m 104
 
2.5%
t 66
 
1.6%
Other values (10) 292
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3413
82.0%
Uppercase Letter 736
 
17.7%
Space Separator 14
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1351
39.6%
r 698
20.5%
v 535
 
15.7%
y 163
 
4.8%
l 149
 
4.4%
a 135
 
4.0%
m 104
 
3.0%
t 66
 
1.9%
o 52
 
1.5%
i 52
 
1.5%
Other values (5) 108
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N 535
72.7%
R 135
 
18.3%
S 52
 
7.1%
V 14
 
1.9%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4149
99.7%
Common 14
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1351
32.6%
r 698
16.8%
N 535
 
12.9%
v 535
 
12.9%
y 163
 
3.9%
l 149
 
3.6%
R 135
 
3.3%
a 135
 
3.3%
m 104
 
2.5%
t 66
 
1.6%
Other values (9) 278
 
6.7%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1351
32.5%
r 698
16.8%
N 535
 
12.9%
v 535
 
12.9%
y 163
 
3.9%
l 149
 
3.6%
R 135
 
3.2%
a 135
 
3.2%
m 104
 
2.5%
t 66
 
1.6%
Other values (10) 292
 
7.0%

Frequency [Hip hop]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Sometimes
218 
Rarely
214 
Never
181 
Very frequently
123 

Length

Max length15
Median length9
Mean length8.1467391
Min length5

Characters and Unicode

Total characters5996
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowRarely
3rd rowRarely
4th rowNever
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Sometimes 218
29.6%
Rarely 214
29.1%
Never 181
24.6%
Very frequently 123
16.7%

Length

2023-07-14T12:30:38.379940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:38.549716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 218
25.4%
rarely 214
24.9%
never 181
21.1%
very 123
14.3%
frequently 123
14.3%

Most occurring characters

ValueCountFrequency (%)
e 1381
23.0%
r 641
10.7%
y 460
 
7.7%
m 436
 
7.3%
t 341
 
5.7%
l 337
 
5.6%
o 218
 
3.6%
S 218
 
3.6%
s 218
 
3.6%
i 218
 
3.6%
Other values (10) 1528
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5137
85.7%
Uppercase Letter 736
 
12.3%
Space Separator 123
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1381
26.9%
r 641
12.5%
y 460
 
9.0%
m 436
 
8.5%
t 341
 
6.6%
l 337
 
6.6%
o 218
 
4.2%
s 218
 
4.2%
i 218
 
4.2%
a 214
 
4.2%
Other values (5) 673
13.1%
Uppercase Letter
ValueCountFrequency (%)
S 218
29.6%
R 214
29.1%
N 181
24.6%
V 123
16.7%
Space Separator
ValueCountFrequency (%)
123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5873
97.9%
Common 123
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1381
23.5%
r 641
10.9%
y 460
 
7.8%
m 436
 
7.4%
t 341
 
5.8%
l 337
 
5.7%
o 218
 
3.7%
S 218
 
3.7%
s 218
 
3.7%
i 218
 
3.7%
Other values (9) 1405
23.9%
Common
ValueCountFrequency (%)
123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1381
23.0%
r 641
10.7%
y 460
 
7.7%
m 436
 
7.3%
t 341
 
5.7%
l 337
 
5.6%
o 218
 
3.6%
S 218
 
3.6%
s 218
 
3.6%
i 218
 
3.6%
Other values (10) 1528
25.5%

Frequency [Jazz]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
261 
Rarely
247 
Sometimes
175 
Very frequently
53 

Length

Max length15
Median length9
Mean length7.0067935
Min length5

Characters and Unicode

Total characters5157
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowVery frequently
3rd rowRarely
4th rowVery frequently
5th rowNever

Common Values

ValueCountFrequency (%)
Never 261
35.5%
Rarely 247
33.6%
Sometimes 175
23.8%
Very frequently 53
 
7.2%

Length

2023-07-14T12:30:38.730853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:38.903545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 261
33.1%
rarely 247
31.3%
sometimes 175
22.2%
very 53
 
6.7%
frequently 53
 
6.7%

Most occurring characters

ValueCountFrequency (%)
e 1278
24.8%
r 614
11.9%
y 353
 
6.8%
m 350
 
6.8%
l 300
 
5.8%
N 261
 
5.1%
v 261
 
5.1%
a 247
 
4.8%
R 247
 
4.8%
t 228
 
4.4%
Other values (10) 1018
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4368
84.7%
Uppercase Letter 736
 
14.3%
Space Separator 53
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1278
29.3%
r 614
14.1%
y 353
 
8.1%
m 350
 
8.0%
l 300
 
6.9%
v 261
 
6.0%
a 247
 
5.7%
t 228
 
5.2%
o 175
 
4.0%
i 175
 
4.0%
Other values (5) 387
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
N 261
35.5%
R 247
33.6%
S 175
23.8%
V 53
 
7.2%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5104
99.0%
Common 53
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1278
25.0%
r 614
12.0%
y 353
 
6.9%
m 350
 
6.9%
l 300
 
5.9%
N 261
 
5.1%
v 261
 
5.1%
a 247
 
4.8%
R 247
 
4.8%
t 228
 
4.5%
Other values (9) 965
18.9%
Common
ValueCountFrequency (%)
53
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1278
24.8%
r 614
11.9%
y 353
 
6.8%
m 350
 
6.8%
l 300
 
5.8%
N 261
 
5.1%
v 261
 
5.1%
a 247
 
4.8%
R 247
 
4.8%
t 228
 
4.4%
Other values (10) 1018
19.7%

Frequency [K pop]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
416 
Rarely
176 
Very frequently
77 
Sometimes
67 

Length

Max length15
Median length5
Mean length6.6494565
Min length5

Characters and Unicode

Total characters4894
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowRarely
3rd rowVery frequently
4th rowSometimes
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Never 416
56.5%
Rarely 176
23.9%
Very frequently 77
 
10.5%
Sometimes 67
 
9.1%

Length

2023-07-14T12:30:39.073763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:39.280000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 416
51.2%
rarely 176
21.6%
very 77
 
9.5%
frequently 77
 
9.5%
sometimes 67
 
8.2%

Most occurring characters

ValueCountFrequency (%)
e 1373
28.1%
r 746
15.2%
N 416
 
8.5%
v 416
 
8.5%
y 330
 
6.7%
l 253
 
5.2%
R 176
 
3.6%
a 176
 
3.6%
t 144
 
2.9%
m 134
 
2.7%
Other values (10) 730
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4081
83.4%
Uppercase Letter 736
 
15.0%
Space Separator 77
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1373
33.6%
r 746
18.3%
v 416
 
10.2%
y 330
 
8.1%
l 253
 
6.2%
a 176
 
4.3%
t 144
 
3.5%
m 134
 
3.3%
u 77
 
1.9%
n 77
 
1.9%
Other values (5) 355
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
N 416
56.5%
R 176
23.9%
V 77
 
10.5%
S 67
 
9.1%
Space Separator
ValueCountFrequency (%)
77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4817
98.4%
Common 77
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1373
28.5%
r 746
15.5%
N 416
 
8.6%
v 416
 
8.6%
y 330
 
6.9%
l 253
 
5.3%
R 176
 
3.7%
a 176
 
3.7%
t 144
 
3.0%
m 134
 
2.8%
Other values (9) 653
13.6%
Common
ValueCountFrequency (%)
77
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1373
28.1%
r 746
15.2%
N 416
 
8.5%
v 416
 
8.5%
y 330
 
6.7%
l 253
 
5.2%
R 176
 
3.6%
a 176
 
3.6%
t 144
 
2.9%
m 134
 
2.7%
Other values (10) 730
14.9%

Frequency [Latin]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
443 
Rarely
172 
Sometimes
88 
Very frequently
 
33

Length

Max length15
Median length5
Mean length6.1603261
Min length5

Characters and Unicode

Total characters4534
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowSometimes
3rd rowNever
4th rowVery frequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Never 443
60.2%
Rarely 172
 
23.4%
Sometimes 88
 
12.0%
Very frequently 33
 
4.5%

Length

2023-07-14T12:30:39.462143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:39.640532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 443
57.6%
rarely 172
 
22.4%
sometimes 88
 
11.4%
very 33
 
4.3%
frequently 33
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 1333
29.4%
r 681
15.0%
N 443
 
9.8%
v 443
 
9.8%
y 238
 
5.2%
l 205
 
4.5%
m 176
 
3.9%
a 172
 
3.8%
R 172
 
3.8%
t 121
 
2.7%
Other values (10) 550
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3765
83.0%
Uppercase Letter 736
 
16.2%
Space Separator 33
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1333
35.4%
r 681
18.1%
v 443
 
11.8%
y 238
 
6.3%
l 205
 
5.4%
m 176
 
4.7%
a 172
 
4.6%
t 121
 
3.2%
o 88
 
2.3%
i 88
 
2.3%
Other values (5) 220
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
N 443
60.2%
R 172
 
23.4%
S 88
 
12.0%
V 33
 
4.5%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4501
99.3%
Common 33
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1333
29.6%
r 681
15.1%
N 443
 
9.8%
v 443
 
9.8%
y 238
 
5.3%
l 205
 
4.6%
m 176
 
3.9%
a 172
 
3.8%
R 172
 
3.8%
t 121
 
2.7%
Other values (9) 517
 
11.5%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1333
29.4%
r 681
15.0%
N 443
 
9.8%
v 443
 
9.8%
y 238
 
5.2%
l 205
 
4.5%
m 176
 
3.9%
a 172
 
3.8%
R 172
 
3.8%
t 121
 
2.7%
Other values (10) 550
12.1%

Frequency [Lofi]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
280 
Rarely
211 
Sometimes
160 
Very frequently
85 

Length

Max length15
Median length9
Mean length7.3111413
Min length5

Characters and Unicode

Total characters5381
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowRarely
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Never 280
38.0%
Rarely 211
28.7%
Sometimes 160
21.7%
Very frequently 85
 
11.5%

Length

2023-07-14T12:30:39.815114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:40.005889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 280
34.1%
rarely 211
25.7%
sometimes 160
19.5%
very 85
 
10.4%
frequently 85
 
10.4%

Most occurring characters

ValueCountFrequency (%)
e 1346
25.0%
r 661
12.3%
y 381
 
7.1%
m 320
 
5.9%
l 296
 
5.5%
N 280
 
5.2%
v 280
 
5.2%
t 245
 
4.6%
a 211
 
3.9%
R 211
 
3.9%
Other values (10) 1150
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4560
84.7%
Uppercase Letter 736
 
13.7%
Space Separator 85
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1346
29.5%
r 661
14.5%
y 381
 
8.4%
m 320
 
7.0%
l 296
 
6.5%
v 280
 
6.1%
t 245
 
5.4%
a 211
 
4.6%
o 160
 
3.5%
i 160
 
3.5%
Other values (5) 500
 
11.0%
Uppercase Letter
ValueCountFrequency (%)
N 280
38.0%
R 211
28.7%
S 160
21.7%
V 85
 
11.5%
Space Separator
ValueCountFrequency (%)
85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5296
98.4%
Common 85
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1346
25.4%
r 661
12.5%
y 381
 
7.2%
m 320
 
6.0%
l 296
 
5.6%
N 280
 
5.3%
v 280
 
5.3%
t 245
 
4.6%
a 211
 
4.0%
R 211
 
4.0%
Other values (9) 1065
20.1%
Common
ValueCountFrequency (%)
85
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1346
25.0%
r 661
12.3%
y 381
 
7.1%
m 320
 
5.9%
l 296
 
5.5%
N 280
 
5.2%
v 280
 
5.2%
t 245
 
4.6%
a 211
 
3.9%
R 211
 
3.9%
Other values (10) 1150
21.4%

Frequency [Metal]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
264 
Rarely
192 
Very frequently
146 
Sometimes
134 

Length

Max length15
Median length9
Mean length7.9728261
Min length5

Characters and Unicode

Total characters5868
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowNever
3rd rowSometimes
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Never 264
35.9%
Rarely 192
26.1%
Very frequently 146
19.8%
Sometimes 134
18.2%

Length

2023-07-14T12:30:40.196870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:40.372679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 264
29.9%
rarely 192
21.8%
very 146
16.6%
frequently 146
16.6%
sometimes 134
15.2%

Most occurring characters

ValueCountFrequency (%)
e 1426
24.3%
r 748
12.7%
y 484
 
8.2%
l 338
 
5.8%
t 280
 
4.8%
m 268
 
4.6%
N 264
 
4.5%
v 264
 
4.5%
R 192
 
3.3%
a 192
 
3.3%
Other values (10) 1412
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4986
85.0%
Uppercase Letter 736
 
12.5%
Space Separator 146
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1426
28.6%
r 748
15.0%
y 484
 
9.7%
l 338
 
6.8%
t 280
 
5.6%
m 268
 
5.4%
v 264
 
5.3%
a 192
 
3.9%
u 146
 
2.9%
n 146
 
2.9%
Other values (5) 694
13.9%
Uppercase Letter
ValueCountFrequency (%)
N 264
35.9%
R 192
26.1%
V 146
19.8%
S 134
18.2%
Space Separator
ValueCountFrequency (%)
146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5722
97.5%
Common 146
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1426
24.9%
r 748
13.1%
y 484
 
8.5%
l 338
 
5.9%
t 280
 
4.9%
m 268
 
4.7%
N 264
 
4.6%
v 264
 
4.6%
R 192
 
3.4%
a 192
 
3.4%
Other values (9) 1266
22.1%
Common
ValueCountFrequency (%)
146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1426
24.3%
r 748
12.7%
y 484
 
8.2%
l 338
 
5.8%
t 280
 
4.8%
m 268
 
4.6%
N 264
 
4.5%
v 264
 
4.5%
R 192
 
3.3%
a 192
 
3.3%
Other values (10) 1412
24.1%

Frequency [Pop]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Very frequently
277 
Sometimes
261 
Rarely
142 
Never
56 

Length

Max length15
Median length9
Mean length10.375
Min length5

Characters and Unicode

Total characters7636
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowSometimes
3rd rowRarely
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Very frequently 277
37.6%
Sometimes 261
35.5%
Rarely 142
19.3%
Never 56
 
7.6%

Length

2023-07-14T12:30:40.729347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:40.924381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
very 277
27.3%
frequently 277
27.3%
sometimes 261
25.8%
rarely 142
14.0%
never 56
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e 1607
21.0%
r 752
 
9.8%
y 696
 
9.1%
t 538
 
7.0%
m 522
 
6.8%
l 419
 
5.5%
n 277
 
3.6%
V 277
 
3.6%
u 277
 
3.6%
q 277
 
3.6%
Other values (10) 1994
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6623
86.7%
Uppercase Letter 736
 
9.6%
Space Separator 277
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1607
24.3%
r 752
11.4%
y 696
10.5%
t 538
 
8.1%
m 522
 
7.9%
l 419
 
6.3%
n 277
 
4.2%
u 277
 
4.2%
q 277
 
4.2%
f 277
 
4.2%
Other values (5) 981
14.8%
Uppercase Letter
ValueCountFrequency (%)
V 277
37.6%
S 261
35.5%
R 142
19.3%
N 56
 
7.6%
Space Separator
ValueCountFrequency (%)
277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7359
96.4%
Common 277
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1607
21.8%
r 752
10.2%
y 696
9.5%
t 538
 
7.3%
m 522
 
7.1%
l 419
 
5.7%
n 277
 
3.8%
V 277
 
3.8%
u 277
 
3.8%
q 277
 
3.8%
Other values (9) 1717
23.3%
Common
ValueCountFrequency (%)
277
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1607
21.0%
r 752
 
9.8%
y 696
 
9.1%
t 538
 
7.0%
m 522
 
6.8%
l 419
 
5.5%
n 277
 
3.6%
V 277
 
3.6%
u 277
 
3.6%
q 277
 
3.6%
Other values (10) 1994
26.1%

Frequency [R&B]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
225 
Rarely
211 
Sometimes
184 
Very frequently
116 

Length

Max length15
Median length9
Mean length7.8627717
Min length5

Characters and Unicode

Total characters5787
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Never 225
30.6%
Rarely 211
28.7%
Sometimes 184
25.0%
Very frequently 116
15.8%

Length

2023-07-14T12:30:41.087914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:41.278524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 225
26.4%
rarely 211
24.8%
sometimes 184
21.6%
very 116
13.6%
frequently 116
13.6%

Most occurring characters

ValueCountFrequency (%)
e 1377
23.8%
r 668
11.5%
y 443
 
7.7%
m 368
 
6.4%
l 327
 
5.7%
t 300
 
5.2%
N 225
 
3.9%
v 225
 
3.9%
a 211
 
3.6%
R 211
 
3.6%
Other values (10) 1432
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4935
85.3%
Uppercase Letter 736
 
12.7%
Space Separator 116
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1377
27.9%
r 668
13.5%
y 443
 
9.0%
m 368
 
7.5%
l 327
 
6.6%
t 300
 
6.1%
v 225
 
4.6%
a 211
 
4.3%
o 184
 
3.7%
i 184
 
3.7%
Other values (5) 648
13.1%
Uppercase Letter
ValueCountFrequency (%)
N 225
30.6%
R 211
28.7%
S 184
25.0%
V 116
15.8%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5671
98.0%
Common 116
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1377
24.3%
r 668
11.8%
y 443
 
7.8%
m 368
 
6.5%
l 327
 
5.8%
t 300
 
5.3%
N 225
 
4.0%
v 225
 
4.0%
a 211
 
3.7%
R 211
 
3.7%
Other values (9) 1316
23.2%
Common
ValueCountFrequency (%)
116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1377
23.8%
r 668
11.5%
y 443
 
7.7%
m 368
 
6.4%
l 327
 
5.7%
t 300
 
5.2%
N 225
 
3.9%
v 225
 
3.9%
a 211
 
3.6%
R 211
 
3.6%
Other values (10) 1432
24.7%

Frequency [Rap]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Rarely
215 
Never
200 
Sometimes
195 
Very frequently
126 

Length

Max length15
Median length9
Mean length8.0638587
Min length5

Characters and Unicode

Total characters5935
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowRarely
3rd rowRarely
4th rowNever
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Rarely 215
29.2%
Never 200
27.2%
Sometimes 195
26.5%
Very frequently 126
17.1%

Length

2023-07-14T12:30:41.456946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:41.668924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rarely 215
24.9%
never 200
23.2%
sometimes 195
22.6%
very 126
14.6%
frequently 126
14.6%

Most occurring characters

ValueCountFrequency (%)
e 1383
23.3%
r 667
11.2%
y 467
 
7.9%
m 390
 
6.6%
l 341
 
5.7%
t 321
 
5.4%
a 215
 
3.6%
R 215
 
3.6%
v 200
 
3.4%
N 200
 
3.4%
Other values (10) 1536
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5073
85.5%
Uppercase Letter 736
 
12.4%
Space Separator 126
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1383
27.3%
r 667
13.1%
y 467
 
9.2%
m 390
 
7.7%
l 341
 
6.7%
t 321
 
6.3%
a 215
 
4.2%
v 200
 
3.9%
o 195
 
3.8%
i 195
 
3.8%
Other values (5) 699
13.8%
Uppercase Letter
ValueCountFrequency (%)
R 215
29.2%
N 200
27.2%
S 195
26.5%
V 126
17.1%
Space Separator
ValueCountFrequency (%)
126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5809
97.9%
Common 126
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1383
23.8%
r 667
11.5%
y 467
 
8.0%
m 390
 
6.7%
l 341
 
5.9%
t 321
 
5.5%
a 215
 
3.7%
R 215
 
3.7%
v 200
 
3.4%
N 200
 
3.4%
Other values (9) 1410
24.3%
Common
ValueCountFrequency (%)
126
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1383
23.3%
r 667
11.2%
y 467
 
7.9%
m 390
 
6.6%
l 341
 
5.7%
t 321
 
5.4%
a 215
 
3.6%
R 215
 
3.6%
v 200
 
3.4%
N 200
 
3.4%
Other values (10) 1536
25.9%

Frequency [Rock]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Very frequently
330 
Sometimes
219 
Rarely
96 
Never
91 

Length

Max length15
Median length9
Mean length10.804348
Min length5

Characters and Unicode

Total characters7952
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowVery frequently
3rd rowRarely
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Very frequently 330
44.8%
Sometimes 219
29.8%
Rarely 96
 
13.0%
Never 91
 
12.4%

Length

2023-07-14T12:30:41.840182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:42.028925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
very 330
31.0%
frequently 330
31.0%
sometimes 219
20.5%
rarely 96
 
9.0%
never 91
 
8.5%

Most occurring characters

ValueCountFrequency (%)
e 1706
21.5%
r 847
10.7%
y 756
 
9.5%
t 549
 
6.9%
m 438
 
5.5%
l 426
 
5.4%
n 330
 
4.1%
V 330
 
4.1%
u 330
 
4.1%
q 330
 
4.1%
Other values (10) 1910
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6886
86.6%
Uppercase Letter 736
 
9.3%
Space Separator 330
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1706
24.8%
r 847
12.3%
y 756
11.0%
t 549
 
8.0%
m 438
 
6.4%
l 426
 
6.2%
n 330
 
4.8%
u 330
 
4.8%
q 330
 
4.8%
f 330
 
4.8%
Other values (5) 844
12.3%
Uppercase Letter
ValueCountFrequency (%)
V 330
44.8%
S 219
29.8%
R 96
 
13.0%
N 91
 
12.4%
Space Separator
ValueCountFrequency (%)
330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7622
95.9%
Common 330
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1706
22.4%
r 847
11.1%
y 756
9.9%
t 549
 
7.2%
m 438
 
5.7%
l 426
 
5.6%
n 330
 
4.3%
V 330
 
4.3%
u 330
 
4.3%
q 330
 
4.3%
Other values (9) 1580
20.7%
Common
ValueCountFrequency (%)
330
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1706
21.5%
r 847
10.7%
y 756
 
9.5%
t 549
 
6.9%
m 438
 
5.5%
l 426
 
5.4%
n 330
 
4.1%
V 330
 
4.1%
u 330
 
4.1%
q 330
 
4.1%
Other values (10) 1910
24.0%

Frequency [Video game music]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Never
236 
Rarely
197 
Sometimes
186 
Very frequently
117 

Length

Max length15
Median length9
Mean length7.8682065
Min length5

Characters and Unicode

Total characters5791
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowRarely
3rd rowVery frequently
4th rowNever
5th rowRarely

Common Values

ValueCountFrequency (%)
Never 236
32.1%
Rarely 197
26.8%
Sometimes 186
25.3%
Very frequently 117
15.9%

Length

2023-07-14T12:30:42.192597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:42.383284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
never 236
27.7%
rarely 197
23.1%
sometimes 186
21.8%
very 117
13.7%
frequently 117
13.7%

Most occurring characters

ValueCountFrequency (%)
e 1392
24.0%
r 667
11.5%
y 431
 
7.4%
m 372
 
6.4%
l 314
 
5.4%
t 303
 
5.2%
N 236
 
4.1%
v 236
 
4.1%
a 197
 
3.4%
R 197
 
3.4%
Other values (10) 1446
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4938
85.3%
Uppercase Letter 736
 
12.7%
Space Separator 117
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1392
28.2%
r 667
13.5%
y 431
 
8.7%
m 372
 
7.5%
l 314
 
6.4%
t 303
 
6.1%
v 236
 
4.8%
a 197
 
4.0%
o 186
 
3.8%
i 186
 
3.8%
Other values (5) 654
13.2%
Uppercase Letter
ValueCountFrequency (%)
N 236
32.1%
R 197
26.8%
S 186
25.3%
V 117
15.9%
Space Separator
ValueCountFrequency (%)
117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5674
98.0%
Common 117
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1392
24.5%
r 667
11.8%
y 431
 
7.6%
m 372
 
6.6%
l 314
 
5.5%
t 303
 
5.3%
N 236
 
4.2%
v 236
 
4.2%
a 197
 
3.5%
R 197
 
3.5%
Other values (9) 1329
23.4%
Common
ValueCountFrequency (%)
117
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1392
24.0%
r 667
11.5%
y 431
 
7.4%
m 372
 
6.4%
l 314
 
5.4%
t 303
 
5.2%
N 236
 
4.1%
v 236
 
4.1%
a 197
 
3.4%
R 197
 
3.4%
Other values (10) 1446
25.0%

Anxiety
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8376359
Minimum0
Maximum10
Zeros35
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:42.542513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7930544
Coefficient of variation (CV)0.47845643
Kurtosis-0.76579107
Mean5.8376359
Median Absolute Deviation (MAD)2
Skewness-0.42134997
Sum4296.5
Variance7.801153
MonotonicityNot monotonic
2023-07-14T12:30:42.714331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 122
16.6%
8 115
15.6%
6 83
11.3%
3 69
9.4%
10 67
9.1%
5 59
8.0%
9 56
7.6%
4 56
7.6%
2 44
 
6.0%
0 35
 
4.8%
Other values (2) 30
 
4.1%
ValueCountFrequency (%)
0 35
 
4.8%
1 29
 
3.9%
2 44
 
6.0%
3 69
9.4%
4 56
7.6%
5 59
8.0%
6 83
11.3%
7 122
16.6%
7.5 1
 
0.1%
8 115
15.6%
ValueCountFrequency (%)
10 67
9.1%
9 56
7.6%
8 115
15.6%
7.5 1
 
0.1%
7 122
16.6%
6 83
11.3%
5 59
8.0%
4 56
7.6%
3 69
9.4%
2 44
 
6.0%

Depression
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7961957
Minimum0
Maximum10
Zeros84
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:42.873810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.02887
Coefficient of variation (CV)0.63151511
Kurtosis-1.1459474
Mean4.7961957
Median Absolute Deviation (MAD)3
Skewness-0.048448873
Sum3530
Variance9.1740535
MonotonicityNot monotonic
2023-07-14T12:30:43.031871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 96
13.0%
2 93
12.6%
6 88
12.0%
0 84
11.4%
8 77
10.5%
3 59
8.0%
4 58
7.9%
5 56
7.6%
10 45
6.1%
1 40
5.4%
Other values (2) 40
5.4%
ValueCountFrequency (%)
0 84
11.4%
1 40
5.4%
2 93
12.6%
3 59
8.0%
3.5 2
 
0.3%
4 58
7.9%
5 56
7.6%
6 88
12.0%
7 96
13.0%
8 77
10.5%
ValueCountFrequency (%)
10 45
6.1%
9 38
 
5.2%
8 77
10.5%
7 96
13.0%
6 88
12.0%
5 56
7.6%
4 58
7.9%
3.5 2
 
0.3%
3 59
8.0%
2 93
12.6%

Insomnia
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7384511
Minimum0
Maximum10
Zeros149
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:43.204558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0886894
Coefficient of variation (CV)0.82619496
Kurtosis-1.0212724
Mean3.7384511
Median Absolute Deviation (MAD)3
Skewness0.41645538
Sum2751.5
Variance9.5400025
MonotonicityNot monotonic
2023-07-14T12:30:43.367323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 149
20.2%
2 88
12.0%
1 82
11.1%
3 68
9.2%
6 62
8.4%
7 59
 
8.0%
4 59
 
8.0%
5 58
 
7.9%
8 49
 
6.7%
10 34
 
4.6%
Other values (2) 28
 
3.8%
ValueCountFrequency (%)
0 149
20.2%
1 82
11.1%
2 88
12.0%
3 68
9.2%
3.5 1
 
0.1%
4 59
 
8.0%
5 58
 
7.9%
6 62
8.4%
7 59
 
8.0%
8 49
 
6.7%
ValueCountFrequency (%)
10 34
 
4.6%
9 27
 
3.7%
8 49
6.7%
7 59
8.0%
6 62
8.4%
5 58
7.9%
4 59
8.0%
3.5 1
 
0.1%
3 68
9.2%
2 88
12.0%

OCD
Real number (ℝ)

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6372283
Minimum0
Maximum10
Zeros248
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-07-14T12:30:43.526920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8420171
Coefficient of variation (CV)1.0776531
Kurtosis-0.12732389
Mean2.6372283
Median Absolute Deviation (MAD)2
Skewness0.95429085
Sum1941
Variance8.0770612
MonotonicityNot monotonic
2023-07-14T12:30:43.681824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 248
33.7%
2 96
 
13.0%
1 95
 
12.9%
3 64
 
8.7%
5 54
 
7.3%
4 48
 
6.5%
7 34
 
4.6%
6 33
 
4.5%
8 28
 
3.8%
10 20
 
2.7%
Other values (3) 16
 
2.2%
ValueCountFrequency (%)
0 248
33.7%
1 95
 
12.9%
2 96
 
13.0%
3 64
 
8.7%
4 48
 
6.5%
5 54
 
7.3%
5.5 1
 
0.1%
6 33
 
4.5%
7 34
 
4.6%
8 28
 
3.8%
ValueCountFrequency (%)
10 20
 
2.7%
9 14
 
1.9%
8.5 1
 
0.1%
8 28
3.8%
7 34
4.6%
6 33
4.5%
5.5 1
 
0.1%
5 54
7.3%
4 48
6.5%
3 64
8.7%

Music effects
Categorical

Distinct3
Distinct (%)0.4%
Missing8
Missing (%)1.1%
Memory size5.9 KiB
Improve
542 
No effect
169 
Worsen
 
17

Length

Max length9
Median length7
Mean length7.4409341
Min length6

Characters and Unicode

Total characters5417
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo effect
2nd rowImprove
3rd rowImprove
4th rowImprove
5th rowImprove

Common Values

ValueCountFrequency (%)
Improve 542
73.6%
No effect 169
 
23.0%
Worsen 17
 
2.3%
(Missing) 8
 
1.1%

Length

2023-07-14T12:30:43.846544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:44.027218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
improve 542
60.4%
no 169
 
18.8%
effect 169
 
18.8%
worsen 17
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 897
16.6%
o 728
13.4%
r 559
10.3%
I 542
10.0%
m 542
10.0%
p 542
10.0%
v 542
10.0%
f 338
 
6.2%
N 169
 
3.1%
169
 
3.1%
Other values (5) 389
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4520
83.4%
Uppercase Letter 728
 
13.4%
Space Separator 169
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 897
19.8%
o 728
16.1%
r 559
12.4%
m 542
12.0%
p 542
12.0%
v 542
12.0%
f 338
 
7.5%
c 169
 
3.7%
t 169
 
3.7%
s 17
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
I 542
74.5%
N 169
 
23.2%
W 17
 
2.3%
Space Separator
ValueCountFrequency (%)
169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5248
96.9%
Common 169
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 897
17.1%
o 728
13.9%
r 559
10.7%
I 542
10.3%
m 542
10.3%
p 542
10.3%
v 542
10.3%
f 338
 
6.4%
N 169
 
3.2%
c 169
 
3.2%
Other values (4) 220
 
4.2%
Common
ValueCountFrequency (%)
169
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 897
16.6%
o 728
13.4%
r 559
10.3%
I 542
10.0%
m 542
10.0%
p 542
10.0%
v 542
10.0%
f 338
 
6.2%
N 169
 
3.1%
169
 
3.1%
Other values (5) 389
7.2%

Permissions
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
I understand.
736 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9568
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI understand.
2nd rowI understand.
3rd rowI understand.
4th rowI understand.
5th rowI understand.

Common Values

ValueCountFrequency (%)
I understand. 736
100.0%

Length

2023-07-14T12:30:44.198560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T12:30:44.367289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
i 736
50.0%
understand 736
50.0%

Most occurring characters

ValueCountFrequency (%)
n 1472
15.4%
d 1472
15.4%
I 736
7.7%
736
7.7%
u 736
7.7%
e 736
7.7%
r 736
7.7%
s 736
7.7%
t 736
7.7%
a 736
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7360
76.9%
Uppercase Letter 736
 
7.7%
Space Separator 736
 
7.7%
Other Punctuation 736
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1472
20.0%
d 1472
20.0%
u 736
10.0%
e 736
10.0%
r 736
10.0%
s 736
10.0%
t 736
10.0%
a 736
10.0%
Uppercase Letter
ValueCountFrequency (%)
I 736
100.0%
Space Separator
ValueCountFrequency (%)
736
100.0%
Other Punctuation
ValueCountFrequency (%)
. 736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8096
84.6%
Common 1472
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1472
18.2%
d 1472
18.2%
I 736
9.1%
u 736
9.1%
e 736
9.1%
r 736
9.1%
s 736
9.1%
t 736
9.1%
a 736
9.1%
Common
ValueCountFrequency (%)
736
50.0%
. 736
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1472
15.4%
d 1472
15.4%
I 736
7.7%
736
7.7%
u 736
7.7%
e 736
7.7%
r 736
7.7%
s 736
7.7%
t 736
7.7%
a 736
7.7%

Interactions

2023-07-14T12:30:25.453334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:04.453674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:07.716719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:19.762826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:21.147363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:22.553070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:23.985727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:25.643323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:06.355957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:07.915734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.019618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:21.404215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:22.772513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:24.184030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:25.834057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:06.560605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:08.105249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.192994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:21.612777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:22.946553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:24.368729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:26.012852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:06.816107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:08.356989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.411311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:21.797877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:23.209795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:24.540365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:26.203677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:07.050734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:08.583007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.580607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:21.995682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:23.388685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:24.723962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:26.415933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:07.249849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:08.763328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.764395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:22.179877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:23.583976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:24.909736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:26.619738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:07.470373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:19.585913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:20.950973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:22.365710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:23.785126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-14T12:30:25.099069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-14T12:30:44.717149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeHours per dayBPMAnxietyDepressionInsomniaOCDPrimary streaming serviceWhile workingInstrumentalistComposerFav genreExploratoryForeign languagesFrequency [Classical]Frequency [Country]Frequency [EDM]Frequency [Folk]Frequency [Gospel]Frequency [Hip hop]Frequency [Jazz]Frequency [K pop]Frequency [Latin]Frequency [Lofi]Frequency [Metal]Frequency [Pop]Frequency [R&B]Frequency [Rap]Frequency [Rock]Frequency [Video game music]Music effects
Age1.000-0.1410.006-0.0690.0090.022-0.0910.2120.1220.1480.0750.1340.1560.1620.1080.1020.0740.1220.1660.1070.0700.0320.0940.1060.0840.1030.0710.1190.1360.1680.117
Hours per day-0.1411.0000.0120.0940.1350.1480.1250.0090.3120.0000.1380.0790.1490.1260.0000.0000.0820.0390.0000.0880.0500.0750.0920.0850.0770.0380.0920.0850.0000.0290.000
BPM0.0060.0121.0000.0310.0520.062-0.0230.0000.0000.0000.0000.0400.0000.0000.0260.0000.0420.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0460.0790.0000.049
Anxiety-0.0690.0940.0311.0000.5120.2940.3430.0000.1160.0000.0000.0510.0000.0970.0000.0410.0580.0000.0730.0340.0000.0000.0490.0830.0840.1000.0000.0630.0560.0750.099
Depression0.0090.1350.0520.5121.0000.3860.2080.0490.0730.0000.0000.0390.0740.0910.0000.0000.0610.0800.0000.0850.0440.0000.0000.0810.1160.0410.0530.0980.0870.0940.147
Insomnia0.0220.1480.0620.2940.3861.0000.2410.0000.0700.0000.0370.0550.0000.0980.0810.0190.0500.0000.0660.0390.0360.0000.0250.0000.0780.0000.0440.0380.0310.0550.000
OCD-0.0910.125-0.0230.3430.2080.2411.0000.0000.0990.0000.0000.0000.0720.0560.0000.0540.0890.0000.0000.0000.0160.0420.0000.0270.0000.0430.0970.0000.0610.0000.000
Primary streaming service0.2120.0090.0000.0000.0490.0000.0001.0000.0770.0000.0000.1400.2720.1590.0000.0560.0440.0300.1400.1140.0530.0790.0690.1090.0360.0870.0770.1170.0590.0740.029
While working0.1220.3120.0000.1160.0730.0700.0990.0771.0000.0700.0240.0000.1310.1110.0000.0890.1420.0000.0000.0250.0810.1570.0840.2040.0000.0650.1010.0740.0000.1220.163
Instrumentalist0.1480.0000.0000.0000.0000.0000.0000.0000.0701.0000.3980.2750.0600.0000.2670.0500.0710.0000.0590.1630.1900.0000.0470.0000.0000.0880.1100.1380.0000.1040.089
Composer0.0750.1380.0000.0000.0000.0370.0000.0000.0240.3981.0000.1350.0860.0000.0780.0600.0000.0540.0760.0520.1870.0420.0000.0000.0810.0000.0000.0270.0000.0000.075
Fav genre0.1340.0790.0400.0510.0390.0550.0000.1400.0000.2750.1351.0000.1940.2480.3760.4200.3700.3120.3230.3680.3060.3800.2160.2480.4440.3110.3220.3470.3660.2980.066
Exploratory0.1560.1490.0000.0000.0740.0000.0720.2720.1310.0600.0860.1941.0000.1750.0520.0000.1210.1250.0770.2240.1610.1680.0690.2450.1410.1890.1700.2490.1520.1000.145
Foreign languages0.1620.1260.0000.0970.0910.0980.0560.1590.1110.0000.0000.2480.1751.0000.1310.0540.2060.0860.0430.1420.0920.3200.2180.1870.0680.0980.1350.1930.0000.1980.000
Frequency [Classical]0.1080.0000.0260.0000.0000.0810.0000.0000.0000.2670.0780.3760.0520.1311.0000.1120.0770.1190.0950.0690.1990.0230.0870.0640.0750.0720.0920.1090.1160.0630.000
Frequency [Country]0.1020.0000.0000.0410.0000.0190.0540.0560.0890.0500.0600.4200.0000.0540.1121.0000.0440.2140.1730.0820.0990.0940.1160.0990.0900.0390.0960.0840.1350.0740.000
Frequency [EDM]0.0740.0820.0420.0580.0610.0500.0890.0440.1420.0710.0000.3700.1210.2060.0770.0441.0000.0430.0600.1710.0590.1600.0730.1660.0640.0910.1110.1530.0720.1500.064
Frequency [Folk]0.1220.0390.0000.0000.0800.0000.0000.0300.0000.0000.0540.3120.1250.0860.1190.2140.0431.0000.1260.0520.0890.1000.1040.0760.1630.0680.0630.0150.2070.0450.000
Frequency [Gospel]0.1660.0000.0000.0730.0000.0660.0000.1400.0000.0590.0760.3230.0770.0430.0950.1730.0600.1261.0000.0870.1370.0540.1380.0710.0540.0000.1510.0780.0500.0500.059
Frequency [Hip hop]0.1070.0880.0000.0340.0850.0390.0000.1140.0250.1630.0520.3680.2240.1420.0690.0820.1710.0520.0871.0000.1470.1620.1380.1850.0280.1870.3300.5880.0720.0770.064
Frequency [Jazz]0.0700.0500.0000.0000.0440.0360.0160.0530.0810.1900.1870.3060.1610.0920.1990.0990.0590.0890.1370.1471.0000.0680.1900.1500.0740.0760.2020.0890.1000.0880.000
Frequency [K pop]0.0320.0750.0000.0000.0000.0000.0420.0790.1570.0000.0420.3800.1680.3200.0230.0940.1600.1000.0540.1620.0681.0000.1790.1590.1030.1970.2060.1660.1130.0910.031
Frequency [Latin]0.0940.0920.0000.0490.0000.0250.0000.0690.0840.0470.0000.2160.0690.2180.0870.1160.0730.1040.1380.1380.1900.1791.0000.1040.0450.1030.2240.1300.0680.0000.016
Frequency [Lofi]0.1060.0850.0000.0830.0810.0000.0270.1090.2040.0000.0000.2480.2450.1870.0640.0990.1660.0760.0710.1850.1500.1590.1041.0000.0670.1540.1520.1500.0000.2090.018
Frequency [Metal]0.0840.0770.0400.0840.1160.0780.0000.0360.0000.0000.0810.4440.1410.0680.0750.0900.0640.1630.0540.0280.0740.1030.0450.0671.0000.1030.1280.0630.3220.1160.000
Frequency [Pop]0.1030.0380.0000.1000.0410.0000.0430.0870.0650.0880.0000.3110.1890.0980.0720.0390.0910.0680.0000.1870.0760.1970.1030.1540.1031.0000.2370.1660.0730.0000.057
Frequency [R&B]0.0710.0920.0000.0000.0530.0440.0970.0770.1010.1100.0000.3220.1700.1350.0920.0960.1110.0630.1510.3300.2020.2060.2240.1520.1280.2371.0000.3340.1100.0190.077
Frequency [Rap]0.1190.0850.0460.0630.0980.0380.0000.1170.0740.1380.0270.3470.2490.1930.1090.0840.1530.0150.0780.5880.0890.1660.1300.1500.0630.1660.3341.0000.0900.0210.000
Frequency [Rock]0.1360.0000.0790.0560.0870.0310.0610.0590.0000.0000.0000.3660.1520.0000.1160.1350.0720.2070.0500.0720.1000.1130.0680.0000.3220.0730.1100.0901.0000.0670.000
Frequency [Video game music]0.1680.0290.0000.0750.0940.0550.0000.0740.1220.1040.0000.2980.1000.1980.0630.0740.1500.0450.0500.0770.0880.0910.0000.2090.1160.0000.0190.0210.0671.0000.030
Music effects0.1170.0000.0490.0990.1470.0000.0000.0290.1630.0890.0750.0660.1450.0000.0000.0000.0640.0000.0590.0640.0000.0310.0160.0180.0000.0570.0770.0000.0000.0301.000

Missing values

2023-07-14T12:30:27.019390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-14T12:30:28.281995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-14T12:30:30.039334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TimestampAgePrimary streaming serviceHours per dayWhile workingInstrumentalistComposerFav genreExploratoryForeign languagesBPMFrequency [Classical]Frequency [Country]Frequency [EDM]Frequency [Folk]Frequency [Gospel]Frequency [Hip hop]Frequency [Jazz]Frequency [K pop]Frequency [Latin]Frequency [Lofi]Frequency [Metal]Frequency [Pop]Frequency [R&B]Frequency [Rap]Frequency [Rock]Frequency [Video game music]AnxietyDepressionInsomniaOCDMusic effectsPermissions
08/27/2022 19:29:0218.0Spotify3.0YesYesYesLatinYesYes156.0RarelyNeverRarelyNeverNeverSometimesNeverVery frequentlyVery frequentlyRarelyNeverVery frequentlySometimesVery frequentlyNeverSometimes3.00.01.00.0NaNI understand.
18/27/2022 19:57:3163.0Pandora1.5YesNoNoRockYesNo119.0SometimesNeverNeverRarelySometimesRarelyVery frequentlyRarelySometimesRarelyNeverSometimesSometimesRarelyVery frequentlyRarely7.02.02.01.0NaNI understand.
28/27/2022 21:28:1818.0Spotify4.0NoNoNoVideo game musicNoYes132.0NeverNeverVery frequentlyNeverNeverRarelyRarelyVery frequentlyNeverSometimesSometimesRarelyNeverRarelyRarelyVery frequently7.07.010.02.0No effectI understand.
38/27/2022 21:40:4061.0YouTube Music2.5YesNoYesJazzYesYes84.0SometimesNeverNeverRarelySometimesNeverVery frequentlySometimesVery frequentlySometimesNeverSometimesSometimesNeverNeverNever9.07.03.03.0ImproveI understand.
48/27/2022 21:54:4718.0Spotify4.0YesNoNoR&BYesNo107.0NeverNeverRarelyNeverRarelyVery frequentlyNeverVery frequentlySometimesSometimesNeverSometimesVery frequentlyVery frequentlyNeverRarely7.02.05.09.0ImproveI understand.
58/27/2022 21:56:5018.0Spotify5.0YesYesYesJazzYesYes86.0RarelySometimesNeverNeverNeverSometimesVery frequentlyVery frequentlyRarelyVery frequentlyRarelyVery frequentlyVery frequentlyVery frequentlyVery frequentlyNever8.08.07.07.0ImproveI understand.
68/27/2022 22:00:2918.0YouTube Music3.0YesYesNoVideo game musicYesYes66.0SometimesNeverRarelySometimesRarelyRarelySometimesNeverRarelyRarelyRarelyRarelyRarelyNeverNeverSometimes4.08.06.00.0ImproveI understand.
78/27/2022 22:18:5921.0Spotify1.0YesNoNoK popYesYes95.0NeverNeverRarelyNeverNeverVery frequentlyRarelyVery frequentlyNeverSometimesNeverSometimesSometimesRarelyNeverRarely5.03.05.03.0ImproveI understand.
88/27/2022 22:33:0519.0Spotify6.0YesNoNoRockNoNo94.0NeverVery frequentlyNeverSometimesNeverNeverNeverNeverNeverNeverVery frequentlyNeverNeverNeverVery frequentlyNever2.00.00.00.0ImproveI understand.
98/27/2022 22:44:0318.0I do not use a streaming service.1.0YesNoNoR&BYesYes155.0RarelyRarelyRarelyRarelySometimesRarelyRarelyNeverRarelyRarelyNeverSometimesSometimesRarelySometimesSometimes2.02.05.01.0ImproveI understand.
TimestampAgePrimary streaming serviceHours per dayWhile workingInstrumentalistComposerFav genreExploratoryForeign languagesBPMFrequency [Classical]Frequency [Country]Frequency [EDM]Frequency [Folk]Frequency [Gospel]Frequency [Hip hop]Frequency [Jazz]Frequency [K pop]Frequency [Latin]Frequency [Lofi]Frequency [Metal]Frequency [Pop]Frequency [R&B]Frequency [Rap]Frequency [Rock]Frequency [Video game music]AnxietyDepressionInsomniaOCDMusic effectsPermissions
72610/23/2022 20:50:2718.0Apple Music18.0YesNoNoEDMYesNo90.0SometimesRarelyVery frequentlyNeverRarelySometimesSometimesNeverNeverSometimesSometimesRarelySometimesSometimesSometimesSometimes9.08.05.010.0ImproveI understand.
72710/26/2022 19:45:5426.0YouTube Music1.0YesNoNoMetalYesYes136.0SometimesRarelySometimesVery frequentlyNeverNeverRarelyNeverNeverRarelyVery frequentlyRarelyNeverNeverNeverRarely0.00.00.00.0No effectI understand.
72810/30/2022 7:24:0814.0Other streaming service7.0YesYesNoCountryYesNo108.0RarelyVery frequentlySometimesSometimesVery frequentlySometimesNeverNeverNeverRarelySometimesVery frequentlySometimesSometimesVery frequentlyRarely7.03.01.02.0ImproveI understand.
72910/30/2022 13:13:3221.0I do not use a streaming service.0.5NoNoNoPopYesNo95.0NeverRarelySometimesNeverNeverSometimesRarelySometimesNeverVery frequentlyNeverVery frequentlySometimesSometimesVery frequentlyNever6.02.02.00.0ImproveI understand.
73010/30/2022 13:15:2621.0Spotify2.0YesNoNoR&BYesYes147.0SometimesNeverSometimesRarelyNeverNeverSometimesVery frequentlyNeverSometimesRarelySometimesVery frequentlySometimesSometimesSometimes7.06.04.06.0ImproveI understand.
73110/30/2022 14:37:2817.0Spotify2.0YesYesNoRockYesYes120.0Very frequentlyRarelyNeverSometimesNeverSometimesRarelyNeverSometimesRarelyRarelyVery frequentlyNeverRarelyVery frequentlyNever7.06.00.09.0ImproveI understand.
73211/1/2022 22:26:4218.0Spotify1.0YesYesNoPopYesYes160.0RarelyRarelyNeverNeverNeverNeverRarelyNeverNeverRarelyNeverVery frequentlyNeverNeverSometimesSometimes3.02.02.05.0ImproveI understand.
73311/3/2022 23:24:3819.0Other streaming service6.0YesNoYesRapYesNo120.0RarelySometimesSometimesRarelyRarelyVery frequentlyRarelyRarelyRarelySometimesRarelySometimesSometimesSometimesRarelyRarely2.02.02.02.0ImproveI understand.
73411/4/2022 17:31:4719.0Spotify5.0YesYesNoClassicalNoNo170.0Very frequentlyNeverNeverNeverNeverNeverRarelyNeverNeverNeverNeverNeverNeverNeverNeverSometimes2.03.02.01.0ImproveI understand.
73511/9/2022 1:55:2029.0YouTube Music2.0YesNoNoHip hopYesYes98.0SometimesRarelyVery frequentlySometimesRarelyVery frequentlyVery frequentlySometimesNeverRarelyNeverSometimesVery frequentlyVery frequentlyVery frequentlyRarely2.02.02.05.0ImproveI understand.